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// See docs in ../ops/candidate_sampling_ops.cc.
#define EIGEN_USE_THREADS
#include <cfloat>
#include <unordered_map>
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/kernels/range_sampler.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/public/tensor_shape.h"
#include "tensorflow/core/util/guarded_philox_random.h"
namespace tensorflow {
class BaseCandidateSamplerOp : public OpKernel {
public:
explicit BaseCandidateSamplerOp(OpKernelConstruction* context)
: OpKernel(context) {
OP_REQUIRES_OK(context, context->GetAttr("num_sampled", &num_sampled_));
OP_REQUIRES_OK(context, context->GetAttr("num_true", &num_true_));
OP_REQUIRES_OK(context, context->GetAttr("unique", &unique_));
OP_REQUIRES_OK(context, generator_.Init(context));
}
void Compute(OpKernelContext* context) override {
const Tensor& true_classes = context->input(0);
OP_REQUIRES(context, true_classes.dims() == 2,
errors::InvalidArgument("true_classes must be a matrix"));
const int32 batch_size = true_classes.dim_size(0);
OP_REQUIRES(context, true_classes.dim_size(1) == num_true_,
errors::InvalidArgument("true_classes must have "
"num_true columns"));
// Output candidates and expected_count.
Tensor* out_sampled_candidates = nullptr;
OP_REQUIRES_OK(context,
context->allocate_output(0, TensorShape({num_sampled_}),
&out_sampled_candidates));
Tensor* out_true_expected_count = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(
1, TensorShape({batch_size, num_true_}),
&out_true_expected_count));
Tensor* out_sampled_expected_count = nullptr;
OP_REQUIRES_OK(context,
context->allocate_output(2, TensorShape({num_sampled_}),
&out_sampled_expected_count));
gtl::ArraySlice<int64> true_candidate(true_classes.matrix<int64>().data(),
batch_size * num_true_);
gtl::MutableArraySlice<int64> sampled_candidate(
out_sampled_candidates->vec<int64>().data(), num_sampled_);
gtl::MutableArraySlice<float> true_expected_count(
out_true_expected_count->matrix<float>().data(),
batch_size * num_true_);
gtl::MutableArraySlice<float> sampled_expected_count(
out_sampled_expected_count->vec<float>().data(), num_sampled_);
CHECK(sampler_) << "CandidateSamplerOp did not set sampler_";
// Approximately conservatively estimate the number of samples required.
// In cases where rejection sampling is used we may occasionally use more
// samples than expected, which will result in reused random bits.
const int64 samples32 = 2048 * num_sampled_;
// Pick sampled candidates.
auto local_gen = generator_.ReserveSamples32(samples32);
random::SimplePhilox random(&local_gen);
sampler_->SampleBatchGetExpectedCount(&random, unique_, &sampled_candidate,
&sampled_expected_count,
true_candidate, &true_expected_count);
if (sampler_->NeedsUpdates()) {
sampler_->Update(true_candidate);
}
}
protected:
void set_sampler(RangeSampler* sampler) { sampler_.reset(sampler); }
private:
int32 num_true_;
int32 num_sampled_;
bool unique_;
std::unique_ptr<RangeSampler> sampler_;
GuardedPhiloxRandom generator_;
};
template <class RangeSamplerType>
class SimpleCandidateSamplerOp : public BaseCandidateSamplerOp {
public:
explicit SimpleCandidateSamplerOp(OpKernelConstruction* context)
: BaseCandidateSamplerOp(context) {
int64 range_max;
OP_REQUIRES_OK(context, context->GetAttr("range_max", &range_max));
set_sampler(new RangeSamplerType(range_max));
}
};
REGISTER_KERNEL_BUILDER(Name("UniformCandidateSampler").Device(DEVICE_CPU),
SimpleCandidateSamplerOp<UniformSampler>);
REGISTER_KERNEL_BUILDER(Name("LogUniformCandidateSampler").Device(DEVICE_CPU),
SimpleCandidateSamplerOp<LogUniformSampler>);
REGISTER_KERNEL_BUILDER(Name("LearnedUnigramCandidateSampler")
.Device(DEVICE_CPU),
SimpleCandidateSamplerOp<UnigramSampler>);
REGISTER_KERNEL_BUILDER(Name("ThreadUnsafeUnigramCandidateSampler")
.Device(DEVICE_CPU),
SimpleCandidateSamplerOp<ThreadUnsafeUnigramSampler>);
class AllCandidateSamplerOp : public BaseCandidateSamplerOp {
public:
explicit AllCandidateSamplerOp(OpKernelConstruction* context)
: BaseCandidateSamplerOp(context) {
int64 range_max;
OP_REQUIRES_OK(context, context->GetAttr("num_sampled", &range_max));
set_sampler(new AllSampler(range_max));
}
};
REGISTER_KERNEL_BUILDER(Name("AllCandidateSampler").Device(DEVICE_CPU),
AllCandidateSamplerOp);
class FixedUnigramCandidateSamplerOp : public BaseCandidateSamplerOp {
public:
explicit FixedUnigramCandidateSamplerOp(OpKernelConstruction* context)
: BaseCandidateSamplerOp(context) {
int64 range_max;
OP_REQUIRES_OK(context, context->GetAttr("range_max", &range_max));
string vocab_file;
OP_REQUIRES_OK(context, context->GetAttr("vocab_file", &vocab_file));
std::vector<float> unigrams;
OP_REQUIRES_OK(context, context->GetAttr("unigrams", &unigrams));
OP_REQUIRES(
context, !vocab_file.empty() || !unigrams.empty(),
errors::InvalidArgument("Must provide either vocab_file or unigrams."));
OP_REQUIRES(context, vocab_file.empty() || unigrams.empty(),
errors::InvalidArgument(
"Must only provide one of vocab_file and unigrams."));
float distortion;
OP_REQUIRES_OK(context, context->GetAttr("distortion", &distortion));
int64 num_reserved_ids;
OP_REQUIRES_OK(context,
context->GetAttr("num_reserved_ids", &num_reserved_ids));
int64 num_shards;
OP_REQUIRES_OK(context, context->GetAttr("num_shards", &num_shards));
int64 shard;
OP_REQUIRES_OK(context, context->GetAttr("shard", &shard));
if (!vocab_file.empty()) {
set_sampler(new FixedUnigramSampler(context->env(), range_max, vocab_file,
distortion, num_reserved_ids,
num_shards, shard));
} else {
set_sampler(new FixedUnigramSampler(range_max, unigrams, distortion,
num_reserved_ids, num_shards, shard));
}
}
};
REGISTER_KERNEL_BUILDER(Name("FixedUnigramCandidateSampler").Device(DEVICE_CPU),
FixedUnigramCandidateSamplerOp);
class ComputeAccidentalHitsOp : public OpKernel {
public:
explicit ComputeAccidentalHitsOp(OpKernelConstruction* context)
: OpKernel(context) {
OP_REQUIRES_OK(context, context->GetAttr("num_true", &num_true_));
}
void Compute(OpKernelContext* context) override {
const Tensor& in_true_candidates = context->input(0);
TensorShape in_true_candidates_shape = in_true_candidates.shape();
OP_REQUIRES(context, TensorShapeUtils::IsMatrix(in_true_candidates_shape) &&
in_true_candidates_shape.dim_size(1) == num_true_,
errors::InvalidArgument(
"true_candidates must be a batch_size * num_true matrix"));
const int64 batch_size = in_true_candidates_shape.dim_size(0);
const Tensor& in_sampled_candidates = context->input(1);
OP_REQUIRES(context,
TensorShapeUtils::IsVector(in_sampled_candidates.shape()),
errors::InvalidArgument(
"sampled_candidates must be a vector, which is typically "
"an output from CandidateSampler"));
std::unordered_map<int64, int> sampled_candidate_to_pos;
for (int64 i = 0; i < in_sampled_candidates.dim_size(0); ++i) {
sampled_candidate_to_pos[in_sampled_candidates.vec<int64>()(i)] = i;
}
// Produce output in the same format as UnpackSparseFeatures.
std::vector<int> indices;
std::vector<int64> ids;
std::vector<float> weights;
for (int64 i = 0; i < batch_size; ++i) {
for (int64 j = 0; j < num_true_; ++j) {
const int64 true_candidate = in_true_candidates.matrix<int64>()(i, j);
const auto look = sampled_candidate_to_pos.find(true_candidate);
if (look != sampled_candidate_to_pos.end()) {
indices.push_back(i);
ids.push_back(look->second);
weights.push_back(-FLT_MAX);
}
}
}
Tensor* out_indices = nullptr;
OP_REQUIRES_OK(
context,
context->allocate_output(
0, TensorShape({static_cast<int>(indices.size())}), &out_indices));
Tensor* out_ids = nullptr;
OP_REQUIRES_OK(
context, context->allocate_output(
1, TensorShape({static_cast<int>(ids.size())}), &out_ids));
Tensor* out_weights = nullptr;
OP_REQUIRES_OK(
context,
context->allocate_output(
2, TensorShape({static_cast<int>(weights.size())}), &out_weights));
for (size_t i = 0; i < indices.size(); ++i) {
out_indices->vec<int32>()(i) = indices[i];
out_ids->vec<int64>()(i) = ids[i];
out_weights->vec<float>()(i) = weights[i];
}
}
private:
int64 num_true_;
};
REGISTER_KERNEL_BUILDER(Name("ComputeAccidentalHits").Device(DEVICE_CPU),
ComputeAccidentalHitsOp);
} // namespace tensorflow
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